Method for identifying adulteration of camellia oleosa seed oil based on triglyceride isomer

Abstract

The invention discloses a method for identifying adulteration of camellia oleosa seed oil based on a triglyceride isomer. The method comprises the following steps: collecting data of primary mass spectrum information and secondary mass spectrum information of glyceride from an oil sample standard substance through an ultra-high performance liquid chromatography-quadrupole flight time-high resolution mass spectrometer; extracting triglyceride compounds with the top 27 peak area contents; carrying out isomer identification and content extraction on the first 27 triglyceride compounds; screening out characteristic isomer compounds according to variable projection importance; establishing an adulteration identification model according to principal component analysis and least square method-discriminant analysis of the isomer content; and adding the sample into the adulteration identification model for identification. According to the adulteration identification model, the camellia oleosa seed oil and the high-oleic-acid oil can be distinguished, the highest accuracy rate of predicting the types of the high-oleic-acid oil can reach 100%, the lowest identification adulteration concentration is 2%, and the adulteration accuracy of the camellia oleosa seed oil is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of analytical detection technology, and particularly relates to a camellia oleifera seed oil adulteration identification method based on triglyceride isomers. BACKGROUND

[0002] Camellia oleifera seed oil is a high-quality woody plant oil unique to China, and its yield ranks first among woody oils. Its material composition is very similar to that of olive oil, and it has always been known as "Oriental olive oil". The content of unsaturated fatty acids in tea oil is as high as 90%, of which the content of oleic acid is more than 75%, so it is an ideal health care edible oil for high blood pressure, high blood lipids, and atherosclerosis. The rich bioactive ingredients such as vitamin E, squalene, and phytosterol in tea oil also have important significance for human health. Due to its high nutritional value, tea oil has become one of the most popular high-quality edible oils in China, and its price is 5-10 times that of ordinary vegetable oil. In order to make high profits, unscrupulous traders often mix cheap oils into camellia oleifera seed oil, leading to frequent adulteration, which seriously affects the excellent quality of camellia oleifera seed oil and the healthy development of the tea oil industry. Under this background, in order to protect the legitimate rights and interests of consumers, the identification of camellia oleifera seed oil has become a practical problem that needs to be solved by researchers.

[0003] The main detection methods for camellia oleifera seed oil adulteration include chromatographic analysis and spectral analysis. Spectral analysis has low sensitivity, and there are serious signal overlaps, unclear markers, and easy overfitting, which leads to experimental results deviation. Chromatographic analysis can accurately locate the characteristic components of different vegetable oils, and the information obtained is more representative. However, for camellia oleifera seed oil with low concentration adulteration and camellia oleifera seed oil mixed with high-oleic oil, it is difficult to accurately identify the fatty acids or triglycerides (using equivalent carbon atoms as analytes) obtained by chromatography alone, and there is an urgent need for a more characteristic camellia oleifera seed oil identification method.

[0004] In recent years, edible oil identification technology has made great progress, but there is still no relatively universal and satisfactory identification technology. According to the difficulty of identifying adulterated tea oil, there are mainly three ways of adulteration: mixing ordinary refined edible oil, mixing high-oleic edible oil, and mixing low-grade tea oil or olive oil. The latter two ways of adulteration are more difficult to identify.

[0005] The advantages of high-resolution mass spectrometry-based lipidomics in counterfeit detection are mainly reflected in its ability to simultaneously detect both macro- and micro-chemical components of oils, obtaining more comprehensive compound information and providing rich information for food counterfeit detection. In the area of ​​tea oil counterfeit detection, the research team led by Zhang Jiukai at the China Academy of Inspection and Quarantine Sciences used high-resolution mass spectrometry to identify characteristic compounds in four types of oils by analyzing different lipid and metabolic components. They established a counterfeit detection model for tea oil adulterated with sunflower oil, rapeseed oil, and soybean oil at adulteration levels exceeding 5%. However, sunflower oil, rapeseed oil, and soybean oil are bulk oils on the market, and their triglyceride levels differ significantly from those of tea seed oil, making them relatively easy cases to detect in tea seed oil. Professor Wang Xingguo's team at Jiangnan University has developed a relatively complex technology for identifying adulterated camellia seed oil. The solution for identifying adulterated camellia seed oil containing high-oleic acid oil combines fatty acids, glycerides (effective carbon number), and secondary metabolites such as sterols, squalene, and tocopherols. This method is quite complex, and there is considerable research on these indicators in camellia oil, making the determination process quite intricate. Furthermore, the industry has reported on the content of these indicators in camellia oil obtained through various processing methods, and the detection methods are relatively mature. While bioactive sterols and squalene are expensive, their content in camellia seed oil is low, suggesting the possibility of deliberate addition. The determination limit for this solution is 10%.

[0006] Therefore, in order to solve the industry problem of adulteration of camellia seed oil with low-grade high-oleic oil, it is necessary to develop a more accurate method for identifying camellia seed oil adulterated with high-oleic edible oil. Summary of the Invention

[0007] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, the first aspect of the present invention proposes a method for identifying adulteration of camellia seed oil based on triglyceride isomers. The adulteration identification method of the present invention can not only comprehensively distinguish between camellia seed oil and high oleic acid oils, but also significantly improve the accuracy of identifying adulteration of camellia seed oil with added high oleic acid oils, while reducing the dependence on the number of samples.

[0008] According to a first aspect of the present invention, a method for identifying adulteration of camellia seed oil based on triglyceride isomers is provided, comprising the following steps:

[0009] S1. Several types of oil standard samples were collected using ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometry (UPLC-QTOF-MS) to obtain primary and secondary mass spectrometry data of glycerides; the data were corrected according to non-targeted lipidomics to obtain analytical data information, and the triglyceride compounds with the highest peak area content were extracted by arranging them from largest to smallest.

[0010] S2. The top 27 triglyceride compounds in step S1 were identified as isomers and their contents were extracted using a pseudo-targeted lipidomics analysis method, and the data were preprocessed. Partial least squares-discriminant analysis was used to analyze the variable projection importance values ​​of the contents of different triglyceride isomers, and characteristic isomer compounds were screened based on the variable projection importance values ​​(VIP).

[0011] S3. Using the content of the characteristic isomers obtained in step S2 as variable parameters, the characteristic isomers are analyzed by principal component analysis and least squares discriminant analysis to establish an adulteration identification model.

[0012] S4. Collect mass spectrometry data of triglyceride isomers of the sample to be tested using an ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer; input the mass spectrometry data into the adulteration identification model in step S3 for identification.

[0013] According to a preferred embodiment of the present invention, in step S1, several types of oil sample standards are injected into the injector of an ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer (UHPLC-QQMS). The standards are separated and analyzed by the UHPLC in the UHPLC-QQMS, and then the mass spectrometry data of the oil sample standards are acquired by the mass spectrometer in the UHPLC-QQMS, with information-dependent acquisition (IDA) set; thus obtaining the primary and secondary mass spectrometry information of glycerides.

[0014] According to a preferred embodiment of the present invention, in step S2, the variable projection importance value (VIP) is set to >1.2 and the p value is less than 0.05 to screen out characteristic isomer compounds.

[0015] According to a preferred embodiment of the present invention, in step S2, the characteristic isomer compounds are shown in the table below:

[0016]

[0017] According to a preferred embodiment of the present invention, in step S1, the conditions for ultra-high performance liquid chromatography (UHPLC) in the ultra-high performance liquid chromatography-quadrupole time-of-flight high-resolution mass spectrometer are as follows:

[0018] Mobile phase A: Prepared from a 5 mM ammonium acetate solution by mixing water, methanol and acetonitrile in a volume ratio of 1:1:1.

[0019] Mobile phase B: prepared from isopropanol to a 20 μM sodium acetate solution; flow rate 0.3 mL / min, column temperature 45℃, injection volume 1 μL; gradient elution program as follows:

[0020] Time (min) Mobile phase A phase Mobile phase B phase 0.0～1.0 80％ 20％ 1.0～3.0 80％～30％ 20％～70％ 3.0～13.0 30％～2％ 70％～98％ 13.0～15.0 2％ 98％ 15.0～15.1 2％～80％ 98％～20％ 15.1～17.0 80％ 20％ .

[0021] According to a preferred embodiment of the present invention, in step S1, the conditions for high-resolution mass spectrometry in the ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer include:

[0022] EAD acquisition conditions: ion source temperature 500-650℃, nebulizer gas pressure 50-60psi, heating gas pressure 50-60psi, curtain gas pressure 30-50psi; declustering voltage 50-100V, EAD collision energy 8-12V, Zeno trap accumulation time 0.05 seconds; EAD electron kinetic energy 10-15eV; electron beam current 6000nA; primary mass spectrometry scan range 200-1200Da.

[0023] According to a preferred embodiment of the present invention, in step S2, the MRM that is intended to target lipidomics HR The EAD (Multiple Reaction Monitoring Mass Spectrometry Quantitative Method Based on High-Resolution Mass Spectrometry and EAD Conditions) conditions are as follows:

[0024] The ion source temperature is 500-650℃, the nebulizer gas pressure is 55psi, the heating gas pressure is 55psi, and the curtain gas pressure is 35psi; the declustering voltage is 80V, the EAD collision energy is 10V, and the Zeno trap accumulation time is 0.05 seconds; the EAD electron kinetic energy is 10-15eV; the electron beam current is 6000nA; and the secondary mass spectrometry scanning range is 260-400Da.

[0025] According to a preferred embodiment of the present invention, in step S1, the correction process includes peak extraction, peak alignment and peak identification using MSDIAL 5.0 software.

[0026] According to a preferred embodiment of the present invention, the MSDIAL 5.0 software is configured as follows:

[0027] QC samples were imported into MSDIAL 5.0 software, and lipid compounds were searched in the EIEIO database. The search conditions were: mass tolerance for primary mass spectrometry was set to 0.010–0.020 Da; mass tolerance for secondary mass spectrometry was set to 0.010–0.030 Da; and retention time tolerance was set to 0.01–0.10 min.

[0028] According to a preferred embodiment of the present invention, the retention time, compound name, compound adduct form and molecular formula of the data exported from MSDIAL 5.0 software are imported into the analytics plugin of SCIEX OS 3.3 software for compound verification, and the peak area of ​​the confirmed compound is extracted.

[0029] According to a preferred embodiment of the present invention, in step S2, the data preprocessing step includes:

[0030] 1. Peak areas of each triglyceride isomer were extracted using the analytics module of SCIEX OS3.3 software. 2. Data preprocessing: The data were normalized by calculating the percentage of each peak area using the peak area normalization method. Chemometric analysis was performed using the Metaboanalyst 6.0 platform. The raw data were preprocessed using a base-10 logarithmic transformation and automatic scaling to make the raw data more consistent with a normal distribution.

[0031] According to a preferred embodiment of the present invention, the oil sample standard includes camellia seed oil, olive oil, high oleic rapeseed oil, high oleic peanut oil, and high oleic sunflower seed oil.

[0032] In this invention, high oleic acid means oleic acid content ≥75%.

[0033] According to a preferred embodiment of the present invention, the camellia seed oil is camellia seed oil prepared by solvent extraction and / or cold pressing.

[0034] According to a preferred embodiment of the present invention, the sample to be tested in step S4 is a pure oil sample or; camellia seed oil is used as the oil to be adulterated, and other oils are used as adulterants. The adulterants and the oil to be adulterated are mixed uniformly in different mass ratios to obtain an experimental sample of adulterated camellia seed oil.

[0035] According to a preferred embodiment of the present invention, the pure oil sample includes olive oil, high-oleic rapeseed oil, high-oleic peanut oil, and high-oleic sunflower oil.

[0036] According to a preferred embodiment of the present invention, the adulterated oil includes at least one of olive oil, high-oleic rapeseed oil, high-oleic peanut oil, and high-oleic sunflower oil.

[0037] According to a preferred embodiment of the present invention, the mixing ratio of the adulterant oil and the original oil is 1% to 100%; the mixing ratio is defined as: mass of adulterant oil / (mass of original oil + mass of adulterant oil) * 100%. For example, the mixing ratio is 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or any sub-range composed of any two of the above values.

[0038] According to a preferred embodiment of the present invention, step S3 further includes verifying the adulteration identification model using a calibration set and a verification set.

[0039] According to a preferred embodiment of the present invention, the dependent variable fit index (R0) is used. 2 Y), Model Predictive Index (Q) 2 To evaluate the predictive ability of adulteration identification models.

[0040] The method for identifying adulteration of camellia seed oil based on triglyceride isomers according to embodiments of the present invention has at least the following beneficial effects:

[0041] There are two main methods for analyzing triglyceride isomers in the existing technology: silver ion liquid chromatography (Ag-UPLC) and non-aqueous reversed liquid chromatography (NARP-UPLC). These two methods can separate some triglyceride isomers, but they have disadvantages such as long analysis time (more than 70 minutes) and poor repeatability of retention time.

[0042] In addition, for camellia seed oil with low concentration of adulteration and camellia seed oil with high oleic acid, it is difficult to accurately identify the fatty acids or triglycerides obtained by chromatography alone.

[0043] Therefore, this invention utilizes high-resolution mass spectrometry (HPLC-quadrupole time-of-flight-high-resolution mass spectrometry) in electron activated dissociation (EAD) fragmentation mode and a pseudo-targeted lipidomics analysis method to obtain the fine structural information of triglycerides and the content of fine structural isomers of this type of compound. Triglyceride isomers were identified as characteristic isomers using variable projection importance values. Principal component analysis and least squares discriminant analysis were then used to analyze these characteristic isomers, and an adulteration identification model was established based on this analysis. This adulteration identification model can not only comprehensively distinguish between camellia seed oil and high-oleic oils simultaneously, achieving a prediction accuracy of up to 100%, but also reduces the predicted adulteration concentration of added high-oleic oils from 10% to 2% in non-targeted lipidomics analysis, significantly improving the accuracy of identifying adulteration of camellia seed oil with added high-oleic oils while reducing dependence on sample quantity.

[0044] Furthermore, the triglyceride isomer compounds used in this method for identifying adulterated camellia seed oil are based on compounds with high content. On the one hand, this ensures the accuracy of compound annotation and facilitates the transfer of methods between different instruments. On the other hand, this method has a particularly strong ability to separate triglyceride isomers and obtains comprehensive information on triglycerides. This is beneficial for guiding the development of methods for determining the content of triglyceride isomers in edible oils using other instruments, thereby establishing a method for identifying high-quality edible oils based on triglyceride isomers using liquid chromatography or other pretreatment methods.

[0045] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0046] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0047] Figure 1 These are the total ion chromatograms (TIC) of five types of oil sample standards according to embodiments of the present invention;

[0048] Figure 2 This is the mass spectrum of TAG54:5 in high oleic peanut oil according to an embodiment of the present invention;

[0049] Figure 3 This is the mass spectrum of TAG54:5 in olive oil according to an embodiment of the present invention;

[0050] Figure 4 This is the mass spectrum of TAG54:5 in camellia seed oil according to an embodiment of the present invention;

[0051] Figure 5 This is the mass spectrum of TAG54:5 in high oleic rapeseed oil according to an embodiment of the present invention;

[0052] Figure 6 This is the mass spectrum of TAG54:5 in high oleic sunflower seed oil according to an embodiment of the present invention;

[0053] Figure 7 VIP diagrams of five types of oil sample standards according to embodiments of the present invention;

[0054] Figure 8 These are heatmaps of triglyceride isomer compounds of five types of oil sample standards according to embodiments of the present invention;

[0055] Figure 9 This is a PCA score chart of five types of oil sample standards according to an embodiment of the present invention;

[0056] Figure 10 It is a PCA score chart of five types of oil sample standards analyzed using traditional high-resolution lipidomics analysis;

[0057] Figure 11 These are orthogonal PLS-DA diagrams of five types of oil sample standards according to embodiments of the present invention;

[0058] Figure 12 This is the permutation test diagram of the PLS-DA model provided by the present invention. Detailed Implementation

[0059] The following are specific embodiments of the present invention, and the technical solutions of the present invention will be further described in conjunction with the embodiments, but the present invention is not limited to these embodiments.

[0060] Unless otherwise specified, the reagents, methods and equipment used in this invention are all conventional reagents, methods and equipment in this technical field.

[0061] Experimental instruments and software:

[0062] Kinetex C18 column (2.1mm × 100mm, 2.6μm, Phenomenex, Inc. (USA); ZenoTOF TM The 7600 mass spectrometer (equipped with an ExionLC AD system consisting of two ExionLC AD pumps, an ExionLC AD autosampler, and an ExionLC AC column oven) is manufactured by Sciex Corporation, USA, and features a vortex mixer, model IKA VORTEX 3.

[0063] Data processing: MSDIAL 5.0 was used for peak extraction and retention time alignment. SCIEX OS 3.3 was used in conjunction with lipid maps to annotate lipid compounds and extract the peak area of ​​each lipid compound. Metaboanalyst 6.0 was used for principal component analysis, partial least squares discriminant analysis and heatmap analysis.

[0064] Sample preparation:

[0065] (1) Five types of oil standard samples: Camellia seed oil (68 samples in total, half of which were selected from Camellia seed oil prepared by solvent extraction and half from Camellia seed oil prepared by cold pressing; commercially available), olive oil (16 samples; commercially available), high oleic rapeseed oil (6 samples; purchased from Nonggu Yihao, Daodaoquan, Longshan Luyuan, Shiyuexiang and Jingxi), high oleic peanut oil (14 samples; purchased from Jinsheng, Jinlongyu, Hema, Daomai, Luhua, Sam's Club and Metro), high oleic sunflower seed oil (7 samples; purchased from Kuiwang, Jinsheng, COFCO Chucui, Nissin Health Liduo and Duoli).

[0066] (2) Using camellia seed oil as the adulterant and olive oil as the adulterant, the adulterant and the adulterant were mixed evenly at mass ratios of 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% to obtain experimental samples of adulterated camellia seed oil I.

[0067] (3) Using camellia seed oil as the base oil and high oleic rapeseed oil as the adulterant oil, the adulterant oil and the base oil were mixed evenly at mass ratios of 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% to obtain experimental samples of adulterated camellia seed oil II.

[0068] (4) Using camellia seed oil as the adulterant and high oleic peanut oil as the adulterant, the adulterant and the adulterant were mixed evenly at mass ratios of 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% to obtain experimental samples of adulterated camellia seed oil III.

[0069] (5) Using camellia seed oil as the base oil and high oleic sunflower seed oil as the adulterant oil, the adulterant oil and the base oil were mixed evenly at mass ratios of 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% to obtain experimental samples of adulterated camellia seed oil IV.

[0070] Traditional identification method: High-resolution CID data acquisition is used, and other steps are the same as those in this application. The CID acquisition conditions are as follows: ion source temperature 500-600℃; spray voltage 5500V; curtain gas 35psi; nebulizer gas 55psi; auxiliary heating gas 55psi; declustering voltage 80V. In full-scan TOF MS, the residence time is 150ms, and the mass scan range is m / z 200–1200. In MS / MS mode, the collision energy is 35eV; the extended collision energy is 15eV; the residence time is 35ms; the mass scan range is m / z 150–1200; and the Zeno trap accumulation time is 0.05 seconds.

[0071] Example

[0072] This example provides a method for identifying adulteration of camellia seed oil based on triglyceride isomers, including the following steps:

[0073] S1. Several types of oil sample standards are injected into the injector of an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometer (UHPLC-QQMS). The standards are separated and analyzed by the UHPLC within the UHPLC-QQMS. Then, the mass spectrometry data of the oil sample standards are acquired by the mass spectrometer within the UHPLC-QQMS. IDA is set to obtain the primary and secondary mass spectrometry information of glycerides. The data is corrected according to non-targeted lipidomics to obtain analytical data. The peak areas are arranged from largest to smallest, and the top 27 triglyceride compounds by peak area content are extracted.

[0074] S2. The top 27 triglyceride compounds in step S1 were identified as isomers and their contents were extracted using a pseudo-targeted lipidomics analysis method, and the data were preprocessed. The variable projection importance values ​​of the contents of different triglyceride isomers were analyzed using partial least squares-discriminant analysis, and characteristic isomer compounds were screened based on the variable projection importance values.

[0075] S3. Using the content of the characteristic isomers obtained in step S2 as variable parameters, the characteristic isomers are analyzed by principal component analysis and least squares discriminant analysis to establish an adulteration identification model.

[0076] S4. Collect mass spectrometry data of the triglyceride isomers of the sample to be tested using high-resolution mass spectrometry; input the mass spectrometry data into the adulteration identification model in step S3 for identification.

[0077] Specifically, in step S1, camellia seed oil, olive oil, high-oleic rapeseed oil, high-oleic peanut oil, and high-oleic sunflower seed oil (diluted to a concentration of 0.5 mg / mL with mass spectrometry-grade isopropanol) are injected through the injector of an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometer. The standards are then separated and analyzed by the ultra-high performance liquid chromatography (UHPLC) instrument within the UHPLC-quadrupole time-of-flight mass spectrometer. The UHPLC conditions are as follows:

[0078] Mobile phase A: Prepared from a 5 mM ammonium acetate solution by mixing water, methanol and acetonitrile in a volume ratio of 1:1:1.

[0079] Mobile phase B: prepared from isopropanol to a 20 μM sodium acetate solution; flow rate 0.3 mL / min, column temperature 45 °C, injection volume 1 μL; gradient elution program as shown in Table 1:

[0080] Table 1

[0081] Time (min) Mobile phase A phase Mobile phase B phase 0.0～1.0 80％ 20％ 1.0～3.0 80％～30％ 20％～70％ 3.0～13.0 30％～2％ 70％～98％ 13.0～15.0 2％ 98％ 15.0～15.1 2％～80％ 98％～20％ 15.1～17.0 80％ 20％

[0082] Test results are as follows Figure 1As shown in the total ion chromatogram (TIC), there was no significant difference in peak time and abundance between camellia seed oil and other high oleic acid oils.

[0083] Furthermore, the oil sample standard was subjected to mass spectrometry data acquisition using the mass spectrometer in the ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer. IDA was set to obtain the primary and secondary mass spectrometry information of the glycerol esters. The high-resolution mass spectrometry conditions were as follows: EAD acquisition conditions: ion source temperature 650℃, nebulizer gas pressure 55psi, heating gas pressure 55psi, curtain gas pressure 35psi; declustering voltage 80V, EAD collision energy 10V, Zeno trap accumulation time 0.05 seconds; EAD electron kinetic energy 13eV; electron beam current 6000nA; primary mass spectrometry scan range 200–1200 Da.

[0084] Further, in step S1, the obtained data is imported into MSDIAL 5.0 software for peak extraction, peak alignment, and peak identification. QC samples are selected and imported into MSDIAL 5.0 software. The EIEIO database is selected for lipid compound retrieval, with the search conditions being a mass tolerance of 0.015 Da for primary mass spectrometry, 0.025 Da for secondary mass spectrometry, and a retention time tolerance of 0.05 min. The retention time, compound name, compound adduct form, and molecular formula from the data exported from MSDIAL 5.0 software are imported into the analytics plugin of SCIEX OS 3.3 software for compound verification. The peak areas of the confirmed compounds are extracted, and the compounds are simply ranked by peak area. The top 20 triglyceride compounds by peak area are extracted.

[0085] Furthermore, in step S2, the top 27 triglyceride compounds were identified as isomers and their contents were extracted using a pseudo-targeted lipidomics analysis method, and the data were preprocessed.

[0086] Specifically, the following uses TAG54:5 as an example to illustrate the detailed process of isomer identification: The mass spectra of TAG54:5 in high-oleic peanut oil, olive oil, camellia seed oil, high-oleic rapeseed oil, and high-oleic sunflower seed oil are shown below. Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, from Figures 2 to 6Analyzing the characteristic fragments generated under the EAD fragmentation mode, such as m / z 329.2450 and 331.2610 in tea oil, these are attributed to characteristic fragments generated by the acyl chain at the sn2 position, which are C18:2 and C18:1 acyl chains at the sn2 position, respectively. Combining this with the characteristic fragments generated at the sn1 and 3 positions, m / z 341.2455, 343.2606, 345.2396, 345.2717, and 347.2573, which are C18:2 and C18:1 chains at the sn1 and 3 positions, respectively, it can be inferred that the triglyceride isomers 18:1 / 18:2 / 18:2 and 18:2 / 18:1 / 18:2 of TAG54:5 in tea seed oil are also present. Similarly, it was deduced that the triglyceride isomers in olive oil and high-oleic rapeseed oil are 18:1 / 18:2 / 18:2, 18:1 / 18:3 / 18:1, 18:2 / 18:1 / 18:2, and 18:1 / 18:1 / 18:3, respectively; the triglyceride isomers in high-oleic peanut oil are 18:1 / 18:2 / 18:2; and the triglyceride isomers in high-oleic sunflower oil are 18:1 / 18:2 / 18:2 and 18:2 / 18:1 / 18:2. The isomer content was determined using MRM HR EAD. The collection conditions for the 27 triglyceride compounds are shown in Table 2 below. Other chromatographic and mass spectrometric collection conditions were consistent with those used for IDA collection. The collected MRM... HR EAD data was used to extract the isomer content of different types of oils for subsequent statistical analysis and data modeling.

[0087] Table 2

[0088]

[0089] In step S2, partial least squares discriminant analysis was used to analyze the variable projected importance (VIP) values ​​of the contents of different triglyceride isomers. The results are as follows: Figure 7As shown, the VIP plot is an important tool in the PLS-DA model for screening key lipid characteristic compounds. The VIP value reflects the contribution of each lipid molecule to the classification model; a higher value indicates a more significant role for the molecule in distinguishing groups. Using a VIP > 1.2 as a threshold, 25 characteristic isomer compounds were screened out from 100 triglyceride isomers. Among them, TAG58:2|18:0 / 18:2 / 22:0, TAG58:3|16:0 / 18:2 / 24:1, TAG60:2|16:0 / 18:1 / 26:1, TAG60:3|18:1 / 18:1 / 24:1, TAG60:4|18:2 / 18:1 / 24:1 and TAG60:2|18:0 / 18:1 / 24:1 have VIP values ​​greater than 2.0, and are present in higher amounts in tea oil, high-oleic rapeseed oil and high-oleic peanut oil, indicating that these three oils have strong characteristic properties. Olive oil also exhibits distinct characteristic compounds. Under a VIP value greater than 1.2, three characteristic isomers of olive oil are identified: TAG50:2|16:0 / 18:1 / 16:1, TAG52:1|16:1_18:0_18:0, and TAG52:3|16:1 / 18:1 / 18:1. In summary, 15 triglyceride isomers were selected as characteristic isomer compounds (see Table 3).

[0090] Table 3

[0091]

[0092] Among them, RT a Represents retention period; confirmed b Representative standard product confirmation; tentative c This is confirmed by spectral libraries such as spectral libraries and lip maps. CY represents camellia seed oil, GL represents olive oil, GCZ represents high-oleic rapeseed oil, GHS represents high-oleic peanut oil, and GKH represents high-oleic sunflower seed oil.

[0093] The chemical names of the 15 characteristic isomers are, in order: stearic acid-2-linoleic acid-3-docosahexaenoic acid triglyceride, 1-palmitoic acid-2-oleic acid-3-docosahexaenoic acid triglyceride, 1-palmitoic acid-2-oleic acid-3-palmitoleic acid, 1-palmitoleic acid-2,3-distearate triglyceride, 1-palmitoleic acid-2,3-dioleic acid triglyceride, 1-palmitoic acid-2-linoleic acid-3-docosahexaenoic acid triglyceride, 1,2-dioleic acid... 1-Linoleic acid-2-oleic acid-3-tetracosenoic acid triglyceride, 1-palmitoic acid-2-oleic acid-3-eicosenoic acid triglyceride, 1-palmitoic acid-2-linoleic acid-3-eicosenoic acid triglyceride, 1-linoleic acid-2-oleic acid-3-eicosenoic acid triglyceride, 1-palmitoyl-2-oleic acid-3-eicosenoic acid triglyceride, 1-palmitoyl-2-linoleic acid-3-eicosenoic acid triglyceride and 1,3-diecosenoic acid-2-oleic acid triglyceride.

[0094] Furthermore, after auto-scaling the preprocessed data, a heatmap was created in MetaboAnalyst 6.0 using the heatmap module. Analysis of variance (ANOVA) was used to select the top 25 data with the largest differences, and a visual heatmap was generated. The heatmap is shown in Table 8. The heatmap clearly shows the five lipid clustering types.

[0095] In step S3, PCA analysis was performed on tea seed oil samples (68 cases), olive oil samples (16 cases), high-oleic rapeseed oil samples (6 cases), high-oleic peanut oil samples (14 cases), and high-oleic sunflower oil samples (7 cases). The PCA score chart is shown below. Figure 9 As can be seen from the score plot, the QC samples are clustered in a small area, indicating good instrument stability and good data quality. Almost all sample data points are within the 95% confidence interval. The projection plots of the five sample groups are clearly distinguishable, indicating significant differences among the five types of high-oleic oils at the triglyceride isomer level. The data within each group are relatively evenly distributed on the PC2 axis projection, suggesting the existence of certain differences within the groups and that the sample selection is representative. The variance contribution rates of PC1 and PC2 are 42.7% and 28.7%, respectively, with a cumulative variance contribution rate of 71.4%. This indicates that the PCA model established using triglyceride isomer content is robust.

[0096] Furthermore, this invention employs conventional high-resolution lipidomics (CID mode mass spectrometry data acquisition) to analyze the PCA score maps of five types of oil sample standards; the results are as follows. Figure 10 As shown, the projection images of the five groups of samples cannot be completely separated, especially the overlapping areas between camellia seed and olive oil, indicating that the lipids of high oleic acid oils are not very different and it is difficult to distinguish them from each other.

[0097] In step S3, supervised PLS-DA is used for discriminant analysis, and its orthogonal PLS-DA diagram is shown below. Figure 11 As shown, the oil samples of the five types of high oleic acid oils clustered into 5 clusters on the PLS-DA score chart.

[0098] The present invention also provides a permutation test diagram for an orthogonal PLS-DA model; such as Figure 12 As shown, after selecting 5 factors, the R-value of the PLS-DA model is... 2 Y = 0.990, Q 2 =0.987, Q 2 This demonstrates the excellence of the model. (R) 2 Y and Q 2 The difference was 0.003, indicating that the model has excellent explanatory and predictive abilities and can accurately predict camellia seed oil, olive oil, high oleic peanut oil, high oleic sunflower seed oil and high oleic rapeseed oil.

[0099] In step S4, the prediction accuracy of the validation and calibration sets of the PLS-DA model is established. Six replicates are set for each additive amount, with 2 / 3 used for the calibration set and 1 / 3 for the validation set. The accuracy of the traditional identification method (using CID mode to acquire mass spectrometry data without identifying triglyceride isomers) and the model based on isomer content of this invention are compared. Details are as follows:

[0100] The testing procedure for adulterated camellia seed oil ranges from 1% to 100%, and the proportion of adulterated oil is as follows:

[0101] Ten camellia seed oil samples were randomly selected. For each type of high-oleic oil (olive oil, high-oleic rapeseed oil, high-oleic peanut oil, and high-oleic sunflower seed oil), six samples were randomly selected. Camellia seed oil was blended with other high-oleic oils at mass ratios of 1%, 2%, 3%, 5%, 7%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. For each concentration, six camellia seed oil samples and six samples of other high-oleic oils were randomly selected and mixed pairwise. Camellia seed oil and other high-oleic oils included, but were not limited to, the samples selected for oil type identification tests. Ninety-six experiments were collected for each of the following combinations: camellia seed oil-olive oil, camellia seed oil-high-oleic rapeseed oil, camellia seed oil-high-oleic peanut oil, and camellia seed oil-high-oleic rapeseed oil. Based on concentration considerations, two-thirds were used to establish the calibration set, and one-third were used for the validation set. The results are shown in Table 4.

[0102] Table 4

[0103]

[0104] Following the same testing method described above, the proportion of adulterated camellia seed oil was tested in the range of 2% to 100%, and the results are shown in Table 5.

[0105] Table 5

[0106]

[0107] Following the same testing method described above, the proportion of adulterated camellia seed oil was tested in the range of 10% to 100%, and the results are shown in Table 6.

[0108] Table 6

[0109]

[0110]

[0111] In summary, based on the data in Tables 4, 5, and 6, this method improves the model's prediction accuracy. Using a prediction accuracy rate higher than 95% as the evaluation standard, the predicted concentration of additives is reduced from 10% in traditional identification methods to 2%. This method significantly improves the accuracy of identifying high-oleic acid oils in camellia seed oil.

[0112] The present invention has been described in detail above with reference to the embodiments of the present invention. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for identifying adulteration of camellia seed oil based on triglyceride isomers, characterized in that, Includes the following steps: S1. Several types of oil standard samples were collected using ultra-high performance liquid chromatography-quadrupole time-of-flight high-resolution mass spectrometry to obtain primary and secondary mass spectrometry data of glycerides; the data were corrected according to non-targeted lipidomics to obtain analytical data information, and the triglyceride compounds with the largest peak area were extracted by arranging them from largest to smallest. S2. The top 27 triglyceride compounds in step S1 were identified as isomers and their contents were extracted using a pseudo-targeted lipidomics analysis method, and the data were preprocessed. The variable projection importance values ​​of the contents of different triglyceride isomers were analyzed using partial least squares-discriminant analysis, and characteristic isomer compounds were screened based on the variable projection importance values. S3. Using the content of the characteristic isomers obtained in step S2 as variable parameters, the characteristic isomers are analyzed by principal component analysis and least squares discriminant analysis to establish an adulteration identification model. S4. Collect mass spectrometry data of triglyceride isomers of the sample to be tested using an ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer; input the mass spectrometry data into the adulteration identification model in step S3 for identification.

2. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, In step S2, the variable projection importance value is set to ≥1.2 and the p value is less than 0.05 to screen out characteristic isomer compounds.

3. The method for identifying adulterated camellia seed oil according to claim 1 or 2, characterized in that, In step S2, the characteristic isomer compounds are shown in the table below:

4. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, In step S1, the conditions for ultra-high performance liquid chromatography (UHPLC) in the ultra-high performance liquid chromatography-quadrupole time-of-flight-high resolution mass spectrometer are as follows: Mobile phase A: Prepared from a 5 mM ammonium acetate solution by mixing water, methanol and acetonitrile in a volume ratio of 1:1:

1. Mobile phase B: prepared from isopropanol to a 20 μM sodium acetate solution; flow rate 0.3 mL / min, column temperature 45℃, injection volume 1 μL; gradient elution program as follows: 。 5. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, In step S1, the conditions for high-resolution mass spectrometry in the ultra-high performance liquid chromatography-quadrupole time-of-flight-high-resolution mass spectrometer include: EAD acquisition conditions: ion source temperature 500-650℃, nebulizer gas pressure 50-60psi, heating gas pressure 50-60psi, curtain gas pressure 30-50psi; declustering voltage 50-100V, EAD collision energy 8-12V, Zeno trap accumulation time 0.05 seconds; EAD electron kinetic energy 10-15eV; electron beam current 6000nA; primary mass spectrometry scan range 200-1200Da.

6. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, In step S2, the MRM that is intended to target lipidomics HR The EAD condition is: The ion source temperature is 500-650℃, the nebulizer gas pressure is 55psi, the heating gas pressure is 55psi, and the curtain gas pressure is 35psi; the declustering voltage is 80V, the EAD collision energy is 10V, and the Zeno trap accumulation time is 0.05 seconds; the EAD electron kinetic energy is 10-15eV; the electron beam current is 6000nA; and the secondary mass spectrometry scanning range is 260-400Da.

7. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, In step S1, the data processing includes peak extraction, peak alignment, and peak identification using MSDIAL 5.0 software.

8. The method for identifying adulterated camellia seed oil according to claim 7, characterized in that, Configure the MSDIAL 5.0 software as follows: QC samples were imported into MSDIAL 5.0 software, and lipid compounds were searched in the EIEIO database. The search conditions were: mass tolerance for primary mass spectrometry was set to 0.010–0.020 Da; mass tolerance for secondary mass spectrometry was set to 0.010–0.030 Da; and retention time tolerance was set to 0.01–0.10 min.

9. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, The oil sample standards include camellia seed oil, olive oil, high-oleic rapeseed oil, high-oleic peanut oil, and high-oleic sunflower seed oil.

10. The method for identifying adulterated camellia seed oil according to claim 1, characterized in that, Step S3 further includes verifying the adulteration identification model using a calibration set and a validation set.

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